# AI chip shortage deepens: NVIDIA Blackwell sold out through mid-2026; CoWoS packaging bottleneck extends to mid-2027

> The bottleneck in AI is shifting from electricity to silicon. Industry reporting indicates NVIDIA's Blackwell GPUs are effectively sold out through mid-2026, with multi-billion-dollar forward orders from Microsoft, Google, Meta, and Amazon placed back in 2025 consuming most of the 2026 and 2027 allocation before smaller buyers get a look in. The deeper constraint is packaging and memory. TSMC's CoWoS advanced packaging, which stitches these chips together, is booked solid through at least mid-2027, and high-bandwidth memory remains scarce even as Samsung and Micron ramp production that will not meaningfully ease supply before late 2026. Increasingly, the limiting factors are not just chips but electricity, copper, and the specialized gases that fabs depend on. For anyone building with AI, the practical planning assumption is constrained supply into at least the third quarter of 2026. That scarcity quietly shapes strategy: it pushes companies toward long-term vendor commitments, rewards those who locked in capacity early, and turns access to compute, rather than model quality alone, into a real competitive divide.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: Data Center Knowledge · Published Thursday, July 23, 2026_

## Wortins' read

The bottleneck in AI is shifting from electricity to silicon. Industry reporting indicates NVIDIA's Blackwell GPUs are effectively sold out through mid-2026, with multi-billion-dollar forward orders from Microsoft, Google, Meta, and Amazon placed back in 2025 consuming most of the 2026 and 2027 allocation before smaller buyers get a look in. The deeper constraint is packaging and memory. TSMC's CoWoS advanced packaging, which stitches these chips together, is booked solid through at least mid-2027, and high-bandwidth memory remains scarce even as Samsung and Micron ramp production that will not meaningfully ease supply before late 2026. Increasingly, the limiting factors are not just chips but electricity, copper, and the specialized gases that fabs depend on. For anyone building with AI, the practical planning assumption is constrained supply into at least the third quarter of 2026. That scarcity quietly shapes strategy: it pushes companies toward long-term vendor commitments, rewards those who locked in capacity early, and turns access to compute, rather than model quality alone, into a real competitive divide.

## Source

[Read the full story at Data Center Knowledge](https://www.datacenterknowledge.com/infrastructure/after-the-power-crunch-ai-infrastructure-hits-a-gpu-wall)

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